These datasets are used in machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field...

paradigm Force control List of important publications in machine learning List of datasets for machine-learning research M-theory (learning framework) The definition...

mix of the smallest and largest Ws. List of datasets for machine learning research Sample complexity Bayesian Optimization Reinforcement learning Improving...

classifiers Cross-validation List of datasets for machine learning research scikit-learn, an open source machine learning library for Python Orange, a free data...

In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating...

list of datasets for machine learning research. It is part of the list of datasets for machine-learning research. These datasets consist primarily of...

theory List of artificial intelligence projects List of datasets for machine learning research History of machine learning Timeline of machine learning Machine...

In statistics and machine learning, leakage (also known as data leakage or target leakage) is the use of information in the model training process which...

List of datasets for machine learning research Hierarchical classification Ron Kohavi; Foster Provost (1998). "Glossary of terms". Machine Learning....

spaces List of datasets for machine learning research Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (2012) Foundations of Machine Learning, The MIT...

In machine learning, a learning curve (or training curve) plots the optimal value of a model's loss function for a training set against this loss function...

Attention is a machine learning method that determines the relative importance of each component in a sequence relative to the other components in that...

includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial...

extraction Feature learning Hashing trick Instrumental variables estimation Kernel method List of datasets for machine learning research Scale co-occurrence...

Multimodal learning, in the context of machine learning, is a type of deep learning using multiple modalities of data, such as text, audio, or images...

language model". The Journal of Machine Learning Research. 3: 1137–1155. "Papers with Code – MLP-Mixer: An all-MLP Architecture for Vision". Haykin, Simon (1998)...

conferences of high impact in machine learning and artificial intelligence research. It is supported by the International Machine Learning Society (IMLS)...

Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves...

Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning...

Mamba is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University...

Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory...

Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020...

machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future...

In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration...

Logic learning machine (LLM) is a machine learning method based on the generation of intelligible rules. LLM is an efficient implementation of the Switching...

A transformer is a deep learning architecture developed by researchers at Google and based on the multi-head attention mechanism, proposed in a 2017 paper...

In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into...

Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled...

In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an agent with human preferences. It involves training a...

Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty"...